trying URL 'http://cran.us.r-project.org/bin/macosx/contrib/4.0/maps_3.3.0.tgz'
Content type 'application/x-gzip' length 3687860 bytes (3.5 MB)
==================================================
downloaded 3.5 MB

The downloaded binary packages are in
    /var/folders/gs/080wb67j58df5y8ccjf53yw00000gn/T//Rtmp9OH1LM/downloaded_packages
trying URL 'http://cran.us.r-project.org/bin/macosx/contrib/4.0/tmap_3.3-1.tgz'
Content type 'application/x-gzip' length 3585561 bytes (3.4 MB)
==================================================
downloaded 3.4 MB

The downloaded binary packages are in
    /var/folders/gs/080wb67j58df5y8ccjf53yw00000gn/T//Rtmp9OH1LM/downloaded_packages
trying URL 'http://cran.us.r-project.org/bin/macosx/contrib/4.0/rgeos_0.5-5.tgz'
Content type 'application/x-gzip' length 8537441 bytes (8.1 MB)
==================================================
downloaded 8.1 MB

The downloaded binary packages are in
    /var/folders/gs/080wb67j58df5y8ccjf53yw00000gn/T//Rtmp9OH1LM/downloaded_packages
library(educationdata)
# Test Run with using get_education_data 
# data <- get_education_data(level = "college-university",
#     source = "ipeds",
#     topic = "directory",
#     filters = list(year = 2019))
# data

# Scorecard data - 2019 
sc <- read_csv('2019_College_Scorecard_Valid_Admissions_Data.csv')
sc

## change projection of sc data
sc <- sc %>%
  dplyr::mutate(uni_rank = case_when(
    ADM_RATE < 0.05 ~ 'elite',
    ADM_RATE < 0.2 ~ 'highly selective',
    ADM_RATE < 0.3 ~ 'more selective',
    ADM_RATE < 0.5 ~ 'selective',
    ADM_RATE < 0.7 ~ 'less selective',
    TRUE ~ 'not selective')) %>% mutate(uni_rank = factor(uni_rank, levels=c('not selective', 'less selective', 'selective', 'more selective', 'highly selective', 'elite')))

Simple Scattergram

First we are going to try to present this pattern for different tiers of universities (admission rate as well as debt)

# Remove PrivacySuppressed Records and transform Debt Median into a numeric value - we can also do this on the main sc df
sc$DEBT_MDN[is.na(sc$DEBT_MDN)] <- 0;

m <- sc %>% subset(DEBT_MDN !='PrivacySuppressed') %>% transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% 
              ggplot(., aes(x=ADM_RATE, y=DEBT_MDN,color=uni_rank)) +
  geom_point() +
  geom_smooth(color='#EA4F88', se = FALSE) +
  theme_ipsum(base_size = 10, axis_title_size = 12, plot_title_size=14) +
  scale_color_viridis_d()+
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()) +
  scale_y_discrete(limits=c(0,10000,20000,30000), labels=c('0','10','20','30')) +
  labs(x='Admissions Rate', y='Median Loan Amount per Student\n(thousands)', 
       title='Student Debt and Admissions Rate',
       color='Selectivity')
Continuous limits supplied to discrete scale.
Did you mean `limits = factor(...)` or `scale_*_continuous()`?
m

Scattergram as Violin Plot

ipeds15 <- get_education_data(level = "college-university",
    source = "ipeds",
    topic = "grad-rates-pell",
    filters = list(year = 2015))
ipeds15

Student Debt over Time

#Process the data 
sc_time <- read_csv('2010_2019_student_debt.csv') 
Missing column names filled in: 'X1' [1]
── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  INSTNM = col_character(),
  CITY = col_character(),
  STABBR = col_character(),
  ZIP = col_character(),
  UNEMP_RATE = col_logical(),
  DEBT_MDN = col_character(),
  MN_EARN_WNE_P10 = col_logical(),
  MD_EARN_WNE_P10 = col_logical(),
  School = col_logical(),
  State = col_logical(),
  True = col_character(),
  ADM_RATE_ALL_1 = col_character()
)
ℹ Use `spec()` for the full column specifications.

28660 parsing failures.
 row    col           expected                  actual                         file
3223 UNITID a double           Community College       '2010_2019_student_debt.csv'
3223 School 1/0/T/F/TRUE/FALSE Kenai Peninsula College '2010_2019_student_debt.csv'
3223 State  1/0/T/F/TRUE/FALSE Alaska                  '2010_2019_student_debt.csv'
3224 UNITID a double           Community College       '2010_2019_student_debt.csv'
3224 School 1/0/T/F/TRUE/FALSE Kodiak College          '2010_2019_student_debt.csv'
.... ...... .................. ....................... ............................
See problems(...) for more details.
sc_time<- sc_time %>% subset(DEBT_MDN !='PrivacySuppressed') %>% 
  transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% 
  dplyr::mutate(DEBT_MDN = ifelse(is.na(DEBT_MDN), 0, DEBT_MDN))
# Additions of States df from Tigris File 
library(tigris)
To enable 
caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.
states <- states(cb = TRUE)

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sc_time
sc_time_year <- sc_time %>% group_by()

# States 
states_10 <- states %>% 
  inner_join(sc_time, by=c(STUSPS='STABBR')) 


popup1 <- paste("State:",states$NAME,"<br/>",
                 "Population:",states$population,"<br/>",
                 "Successful Projects / Population:",states$scaled_success,"<br/>")

#used 'success' measures. 
m <- leaflet(states) %>% addProviderTiles("CartoDB.Positron") %>%
  addPolygons(fillColor = ~pal(states$scaled_success),
              color = "white",
              weight = 0.5,
              fillOpacity = 0.7,  
              highlight = highlightOptions(
                weight = 5,
                color = "#666",
                fillOpacity = 0.7,
                bringToFront = TRUE),
                popup=popup1) %>%
  addLegend(position = "bottomleft",pal = pal, values = c(min(states$scaled_success),max(states$scaled_success)),title = "Successful Kickstarter Projects\n (scaled by state)") %>% 
  setView(-98.5795, 39.8282, zoom=3)
Error in setView(., -98.5795, 39.8282, zoom = 3) : 
  could not find function "setView"
---
title: "Visuals_Draft_04_14"
author: "Connie Xu"
date: "4/14/2021"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r packages, echo=FALSE, eval=TRUE, warning=FALSE, message=FALSE}
r = getOption("repos")
r["CRAN"] = "http://cran.us.r-project.org"
options(repos = r)
# install.packages (basic)
suppressMessages(library(dplyr))
suppressMessages(library(tidyverse))

# install.packages (reading)
suppressMessages(library(XML))
suppressMessages(library(RCurl))
suppressMessages(library(readr))
suppressMessages(library("readxl"))

# install.packages (themes)
suppressMessages(library(ggthemes))
suppressMessages(library(ggrepel))
suppressMessages(library(RColorBrewer))
suppressMessages(library(viridis))
suppressMessages(library(hrbrthemes))
suppressMessages(library(plotly))


# install.packages (maps)
suppressMessages(library(RgoogleMaps))
suppressMessages(library(ggmap))
suppressMessages(install.packages("maps"))
suppressMessages(install.packages("tmap")) # install the CRAN version
suppressMessages(library(tmap))
suppressMessages(install.packages('rgeos'))

suppressMessages(library(devtools))
# Let's install the development version from Github. Run
suppressMessages(devtools::install_github("rstudio/leaflet"))
```

```{r import general data, echo=TRUE, eval=TRUE, message=FALSE, warning=FALSE}
library(educationdata)
# Test Run with using get_education_data 
# data <- get_education_data(level = "college-university",
#     source = "ipeds",
#     topic = "directory",
#     filters = list(year = 2019))
# data

# Scorecard data - 2019 
sc <- read_csv('2019_College_Scorecard_Valid_Admissions_Data.csv')
sc

## change projection of sc data
sc <- sc %>%
  dplyr::mutate(uni_rank = case_when(
    ADM_RATE < 0.05 ~ 'elite',
    ADM_RATE < 0.2 ~ 'highly selective',
    ADM_RATE < 0.3 ~ 'more selective',
    ADM_RATE < 0.5 ~ 'selective',
    ADM_RATE < 0.7 ~ 'less selective',
    TRUE ~ 'not selective')) %>% mutate(uni_rank = factor(uni_rank, levels=c('not selective', 'less selective', 'selective', 'more selective', 'highly selective', 'elite')))
```

## Simple Scattergram 

First we are going to try to present this pattern for different tiers of universities (admission rate as well as debt)
```{r, echo=TRUE, eval=TRUE}
# Remove PrivacySuppressed Records and transform Debt Median into a numeric value - we can also do this on the main sc df
sc$DEBT_MDN[is.na(sc$DEBT_MDN)] <- 0;

m <- sc %>% subset(DEBT_MDN !='PrivacySuppressed') %>% transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% 
              ggplot(., aes(x=ADM_RATE, y=DEBT_MDN,color=uni_rank)) +
  geom_point() +
  geom_smooth(color='#EA4F88', se = FALSE) +
  theme_ipsum(base_size = 10, axis_title_size = 12, plot_title_size=14) +
  scale_color_viridis_d()+
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()) +
  scale_y_discrete(limits=c(0,10000,20000,30000), labels=c('0','10','20','30')) +
  labs(x='Admissions Rate', y='Median Loan Amount per Student\n(thousands)', 
       title='Student Debt and Admissions Rate',
       color='Selectivity')
m
```
```{r, echo=TRUE, eval=TRUE}
# Create Data Table (Summarized) for 
library('scales')

sc_dt <- sc %>% subset(DEBT_MDN !='PrivacySuppressed') %>% transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% group_by(uni_rank) %>% mutate(`Number of Universities` = n()) %>% ungroup() %>% mutate(DEBT_MDN_STUDENTS = DEBT_MDN*UGDS) %>% group_by(uni_rank) %>% mutate(`Median Student Loans` = paste('$',round(sum(DEBT_MDN_STUDENTS, na.rm=TRUE)/sum(UGDS, na.rm=TRUE),2))) %>% 
  mutate(`Min Acceptance Rate` = percent(min(ADM_RATE))) %>% mutate(`Max Acceptance Rate` = percent(max(ADM_RATE))) %>% ungroup() %>% 
  group_by(uni_rank,`Median Student Loans`,`Number of Universities`,`Min Acceptance Rate`,`Max Acceptance Rate`) %>% 
  summarize()

install.packages('DT')
library(DT)
table <- datatable(sc_dt,style = "default",filter = 'top',  caption = 'Universities and Selectivity')
table

```


## Scattergram as Violin Plot
```{r, echo=TRUE, eval=TRUE}
m <- sc %>% subset(DEBT_MDN !='PrivacySuppressed') %>% transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% 
  ggplot(., aes(x=uni_rank, y=DEBT_MDN)) +
  geom_violin(aes(fill=uni_rank,color=uni_rank)) +
  geom_boxplot(width = 0.2)+
  theme_ipsum(base_size = 10, axis_title_size = 12, plot_title_size=14) +
  scale_fill_viridis_d() + 
  scale_color_viridis_d() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()) +
  scale_y_discrete(limits=c(0,10000,20000,30000), labels=c('0','10','20','30')) +
  scale_x_discrete(labels=c('not\nselective\n(>70%)','less\nselective\n(50%-70%)',
                            'selective\n(30%-50%)','more\nselective\n(20%-30%)','highly\nselective\n(5%-20%)','elite\n(<5%)')) +

  labs(x='Selectivity\n(admission rate thresholds)', y='Median Loan Amount per Student\n(thousands)', 
       title='Selective Schools and Student Debt',
       color='',fill='')
m
```

```{r, echo=TRUE, eval=TRUE}
ipeds15 <- get_education_data(level = "college-university",
    source = "ipeds",
    topic = "grad-rates-pell",
    filters = list(year = 2015))
ipeds15
```


## Student Debt over Time

```{r, echo=TRUE, eval=TRUE}
#Process the data 
sc_time <- read_csv('2010_2019_student_debt.csv') 

sc_time<- sc_time %>% subset(DEBT_MDN !='PrivacySuppressed') %>% 
  transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% 
  dplyr::mutate(DEBT_MDN = ifelse(is.na(DEBT_MDN), 0, DEBT_MDN))

```


```{r, echo=TRUE, eval=TRUE}
#Process the data 
sc_time <- read_csv('2009_2019_student_debt.csv') 

sc_time<- sc_time %>% subset(DEBT_MDN !='PrivacySuppressed') %>% 
  transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% 
  dplyr::mutate(DEBT_MDN = ifelse(is.na(DEBT_MDN), 0, DEBT_MDN)) %>% 
  mutate(DEBT_MDN_STUDENT = DEBT_MDN*UGDS)
sc_time
sum(sc_time$UGDS,na.rm=TRUE)
```

```{r, echo=TRUE, eval=TRUE}
# CPI Inflation Rates - Got Average Yearly Inflation Rate for Scaling for Student Debt 
install.packages('quantmod')
library(quantmod)
getSymbols("CPIAUCSL", src='FRED')
avg.cpi <- apply.yearly(CPIAUCSL, mean)
cf <- as.data.frame(avg.cpi/as.numeric(avg.cpi['2009'])) 
cf$Year_Ending <- format(as.Date(row.names(cf), format="%Y-%m-%d"),"%Y")

# Merged for Inflation 
sc_time_df <- sc_time %>% group_by(`Year_Ending`) %>% mutate(`Average Annual Student Debt - National` = sum(DEBT_MDN_STUDENT,na.rm=TRUE)/sum(UGDS,na.rm=TRUE)) %>% ungroup() %>% 
  dplyr::mutate(uni_rank = case_when(
    ADM_RATE < 0.05 ~ 'elite',
    ADM_RATE < 0.2 ~ 'highly selective',
    ADM_RATE < 0.3 ~ 'more selective',
    ADM_RATE < 0.5 ~ 'selective',
    ADM_RATE < 0.7 ~ 'less selective',
    TRUE ~ 'not selective')) %>%
  mutate(uni_rank = factor(uni_rank, levels=c('not selective', 'less selective', 'selective', 
                                              'more selective', 'highly selective', 'elite'))) %>%
  group_by(uni_rank,Year_Ending) %>% 
  mutate(`Average Annual Student Debt (by Selectivity)` = sum(DEBT_MDN_STUDENT,na.rm=TRUE)/sum(UGDS,na.rm=TRUE)) %>% 
  ungroup() %>% 
  group_by(`Year_Ending`,`Average Annual Student Debt (by Selectivity)`,
           uni_rank,`Average Annual Student Debt - National`) %>% summarize() %>% 
  merge(cf) %>% 
  mutate(`Adjusted Average Annual Student Debt` = `Average Annual Student Debt (by Selectivity)`/
           CPIAUCSL) %>% 
  mutate(`Adjusted Average Annual Student Debt - Composite` = `Average Annual Student Debt - National`/
           CPIAUCSL)

sc_df <- sc_time_df %>% group_by(`Average Annual Student Debt - National`,`Adjusted Average Annual Student Debt - Composite`,Year_Ending) %>% summarize() %>% mutate(uni_rank='national average') %>% mutate(`Adjusted Average Annual Student Debt`=`Adjusted Average Annual Student Debt - Composite`) %>% dplyr::mutate(`Average Annual Student Debt (by Selectivity)` = `Average Annual Student Debt - National`) %>% merge(cf) %>% select(Year_Ending,`Average Annual Student Debt (by Selectivity)`, uni_rank, `Average Annual Student Debt - National`, CPIAUCSL, `Adjusted Average Annual Student Debt`,`Adjusted Average Annual Student Debt - Composite`)
sc_time_df <- sc_time_df %>% rbind(sc_df) %>% mutate(uni_rank = factor(uni_rank, levels=c('national average','not selective', 'less selective', 'selective', 
                                              'more selective', 'highly selective', 'elite')))
sc_time_df

p <- sc_time_df %>% 
  ggplot(.,aes(x=Year_Ending,y=`Adjusted Average Annual Student Debt`, color=uni_rank)) + 
  geom_point() + geom_line() + 
  theme_ipsum(base_size = 10, axis_title_size = 12, plot_title_size=14) +
  scale_color_viridis_d()+
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()) +
  scale_x_continuous(breaks = round(seq(min(sc_time$Year_Ending), max(sc_time$Year_Ending), by = 2),1)) +
  labs(x='', y='Median Loan Amount per Student\n(thousands)', 
       title='Student Debt Has Been Rising Over The Years',
       color='',fill='')
ggplotly(p)

fig1 <- sc_time_df %>% plot_ly(x = ~Year_Ending, y = ~`Adjusted Average Annual Student Debt`, type = 'scatter',
  color = ~uni_rank, alpha=0.8, hovertemplate = 'Average Debt/Student (USD): %{y}% <extra></extra>') %>% add_lines(x = ~Year_Ending,y = ~`Adjusted Average Annual Student Debt`,line = list(color =~uni_rank)) 
?linetype

fig1


```


```{r, echo=TRUE, eval=TRUE }
# Additions of States df from Tigris File 
library(tigris)
states <- states(cb = TRUE)
sc_time
sc_time_year <- sc_time %>% group_by()

# States 
states_10 <- states %>% 
  inner_join(sc_time, by=c(STUSPS='STABBR')) 


popup1 <- paste("State:",states$NAME,"<br/>",
                 "Population:",states$population,"<br/>",
                 "Successful Projects / Population:",states$scaled_success,"<br/>")

#used 'success' measures. 
m <- leaflet(states) %>% addProviderTiles("CartoDB.Positron") %>%
  addPolygons(fillColor = ~pal(states$scaled_success),
              color = "white",
              weight = 0.5,
              fillOpacity = 0.7,  
              highlight = highlightOptions(
                weight = 5,
                color = "#666",
                fillOpacity = 0.7,
                bringToFront = TRUE),
                popup=popup1) %>%
  addLegend(position = "bottomleft",pal = pal, values = c(min(states$scaled_success),max(states$scaled_success)),title = "Successful Kickstarter Projects\n (scaled by state)") %>% 
  setView(-98.5795, 39.8282, zoom=3)


library(dplyr)
library(leaflet)

# Fake data
df <- data.frame(lng = c(-5, -5, -5, -5, -15, -15, -10),
               lat = c(8, 8, 8, 8, 33, 33, 20),
               year = c(2018, 2018, 2018, 2017, 2017, 2017, 2016),
               stringsAsFactors = FALSE)

ui <- bootstrapPage(
tags$style(type = "text/css", "html, body {width:100%;height:100%}"),
leafletOutput("map", width = "100%", height = "100%"),
absolutePanel(top = 10, right = 10,
              style="z-index:500;", # legend over my map (map z = 400)
              tags$h3("map"), 
              sliderInput("periode", "Chronology",
                          min(df$year),
                          max(df$year),
                          value = range(df$year),
                          step = 1,
                          sep = ""
              )
)
)

server <- function(input, output, session) {

# reactive filtering data from UI

reactive_data_chrono <- reactive({
  df %>%
    filter(year >= input$periode[1] & year <= input$periode[2])
})


# static backround map
output$map <- renderLeaflet({
  leaflet(df) %>%
    addTiles() %>%
    fitBounds(~min(lng), ~min(lat), ~max(lng), ~max(lat))
})  

# reactive circles map
observe({
  leafletProxy("map", data = reactive_data_chrono()) %>%
    clearShapes() %>%
    addMarkers(lng=~lng,
               lat=~lat,
               layerId = ~id) # Assigning df id to layerid
})
}

shinyApp(ui, server)

```



